Computerized decisioning systems are becoming increasingly leveraged by organizations to support decision making activities associated with the organizations. For instance, organizations use these decisioning systems to compile useful information concerning organizational activities, analyze the information, and/or propose decisions stemming from the organizational activities. In the financial sector, for example, financial institutions use computerized decisioning systems to conduct market analyses, monitor for fraud detection, determine credit limit approvals, make investment decisions, etc.
These computerized decisioning systems use “attributes” when performing operations for an organization. These attributes define and/or summarize properties of an underlying object, activity, customer, etc. For example, a credit attribute may be generated by a financial institution to summarize raw credit data for an individual into one value that can be leveraged for credit decisioning. Currently, however, computer programmers must “code” the same attributes across multiple environments, such as an analytical environment (e.g., where the attributes are used for internal testing) and a production environment (e.g., where the same attributes are used after passing internal testing to make decisions that impact an entity external to the financial institution, like a credit-seeking consumer). Further, computer programmers must also “code” the same attributes across different decisioning systems (e.g., marketing systems, application processing systems, account management systems, etc.).
Thus, attributes must currently be coded and tested multiple times between the analytical to production rollout phases across multiple systems. This leads to long lead-times to put an attribute to use in production environments. Further, having to code the same attribute multiple times increases the chance of errors. Also, coding the same attribute multiple times may lead to inconsistent attribute data between analytical and production environments, as well as between different decisioning systems (e.g., marketing, application, account management, etc.). This in turn can lead to a suboptimal customer experience.